Abstract
In this paper, a new sequential joint -numerical radius is introduced for sequences of bounded linear operators on Hilbert spaces, motivated by recent generalizations of the numerical radius. Firstly, several fundamental properties of the sequential joint -numerical radius function are established. Subsequently, lower and upper bounds for the sequential joint -numerical radius function are derived, yielding generalizations of several known inequalities. Finally, the sequential joint -numerical radius function is investigated for operator sequences whose coordinate operators are sectorial, leading to further estimates and properties. The obtained results extend and unify a variety of existing results on numerical radius inequalities and provide new contributions to the theory of operator sequences on Hilbert spaces.
MSC:
47A12; 47A30; 47A63; 47B44; 15A60
1. Introduction
The numerical range and the numerical radius of an operator have been investigated extensively in operator theory and have been related to various problems arising in functional analysis and its applications. In particular, several properties and inequalities involving the numerical radius have been established in connection with operator theory, numerical analysis, and related areas [1,2,3]. The investigation of numerical radius inequalities and their refinements has attracted considerable attention in the theory of operators on Hilbert spaces.
Throughout this paper, a complex Hilbert space will be denoted by H, with inner product and associated norm . The algebra of all bounded linear operators on H will be denoted by , and the unit sphere of H will be denoted by .
For an operator , the numerical radius is given by
The operator norm of is given by
For a general operator , the exact value of the numerical radius is usually difficult to determine.
Closed-form expressions can generally be obtained only when additional structural properties of the operator are available.
For this reason, considerable attention has been given to establishing upper and lower estimates for .
The numerical radius is known to define a norm on . A fundamental pair of estimates for is given by the following two-sided inequality (see, e.g., [4]):
Equality in the upper estimate in (1) is attained whenever A is normal, whereas equality in the lower estimate is obtained if , where 0 denotes the zero operator in .
Several refinements of (1) have been established in the literature. In particular, the following two-sided estimate will be used here; see [5,6]:
The inequality (3) refines both the left and right inequalities of (1).
Besides the classical numerical radius, the -numerical range and the associated -numerical radius provide an extension of this concept. The corresponding definition is recalled below.
Definition 1
([7]). Let and let . The δ-numerical range associated with A is given by
where stands for the closure of the corresponding set in the complex plane.
The set is a closed subset of the closure of the classical numerical range. By applying a slight modification of a theorem due to Dekker [8], its connectedness can be established. The convexity of is also of particular interest; in particular, convexity is ensured when A is normal or when H has dimension two.
Definition 2.
Let with . The quantities and are introduced, respectively, by
and
The -numerical radius was introduced by Cho and Takaguchi [9] based on the earlier ideas of Stampfli [7].
Several estimates for the numerical radius have been obtained in terms of the norm of the operator, its adjoint, its modulus, and combinations of these quantities. Further details can be found in [4,10,11,12,13,14,15,16,17,18,19,20,21].
Another important extension of the classical numerical range and the classical numerical radius is provided by the concept of the q-numerical range and the corresponding q-numerical radius.
Definition 3
([22]). Let and satisfy The q-numerical range and the q-numerical radius of A are defined, respectively, as
and
where
For , it is readily seen that coincides with the classical numerical range , and hence . The q-numerical range was first introduced by Marcus and Andersen [23] in 1977 for a linear transformation A defined on an n-dimensional unitary space. The convexity of the q-numerical range was established by Tsing [24]. Various properties of were subsequently studied by Li et al. [25] and Li and Nakazato [26]. Chien and Nakazato [27] described the boundary of a square matrix’s q-numerical range by employing the concept of the Davis–Wielandt shell. The q-numerical ranges associated with shift operators were also investigated in [28,29]. Duan [30] highlighted the significance of the q-numerical range in characterizing the perfect distinguishability of quantum operations. More recently, Moghaddam et al. [31] investigated several bounds for the q-numerical radius. Some of the bounds obtained in that work are listed below:
These inequalities generalize the corresponding inequalities in (1), (2), and (3), respectively, for the classical numerical radius.
A sharper lower bound for the q-numerical radius was obtained in [22], namely,
Another important class of operators that will be used later in this paper is one special class of sectorial operators.
Definition 4.
For any fixed let
- 1.
- An operator , with , is called -sectorial on a Hilbert space H whenever
- 2.
- The notation will be used for the class of all -sectorial operators on the Hilbert space H, with vertex at the origin and half-angle .
Note that each operator can be expressed in the Cartesian decomposition form as where and Here, denotes the Hilbert adjoint of Throughout this study we denote by the absolute value of an operator Also, we define
Motivated by these developments and the recent study of joint -numerical radius functions in [32], in this paper we introduce a new sequential joint -numerical radius for operator sequences on Hilbert spaces. The choice of the parameters f, , and q is motivated by three complementary directions in the existing generalizations of the numerical radius. The function f provides a flexible scalarizing mechanism, while incorporates a lower-bound constraint on the relevant operator quantities, and q controls the geometric relation between the vectors involved through the condition . Thus, rather than being intended as a canonical choice among all possible extensions, the triple is designed to combine these three established features in a single framework for operator sequences. The special cases listed in Section 2 demonstrate that this choice simultaneously recovers several previously studied numerical-radius-type notions.
The use of operator sequences, rather than only finite operator tuples, allows the proposed framework to capture the collective behavior of an entire sequence of operators. In this setting, the summability condition ensures that the contribution of the infinitely many coordinates remains controlled. Finite operator tuples can then be viewed as particular cases obtained by taking all but finitely many coordinates to be zero, whereas the sequential framework also accommodates genuinely infinite families. Thus, the proposed numerical radius is not merely a formal extension of finite-dimensional tuples, but provides a unified way to measure the overall numerical-radius behavior of an operator sequence.
This work is organized as follows: In Section 2, the basic definitions and preliminary results required throughout the paper are presented. In Section 3, several fundamental properties of the sequential joint -numerical radius function are established, and lower and upper bounds for this function are derived. Finally, Section 4 is devoted to the investigation of the sequential joint -numerical radius function for operator sequences whose coordinate operators are sectorial.
2. Basic Definitions and Preliminaries
In this section, the basic definitions and preliminary results required for the subsequent analysis are presented.
In order to extend the classical numerical radius concept to a more general setting, operator sequences and their associated operations are first introduced.
Definition 5.
Let and be two operator sequences with for every , and let . The following notions are defined:
- 1.
- if and only if for every .
- 2.
- .
- 3.
- .
- 4.
- .
- 5.
- .
- 6.
- and .
- 7.
- .
- 8.
- and .
- 9.
- The operator norm of the operator sequence is defined as
- 10.
- .
Based on the above definition, the following function, referred to as a function-norm (although it is not necessarily a norm in the usual sense), is introduced. This allows the introduction of a new definition of the numerical radius, which will be considered throughout the paper.
Definition 6.
Let be a continuous strictly increasing surjective function. Consider a sequence of linear bounded operators on H satisfying the summability condition Define the following:
- 1.
- .
- 2.
- .
Using the above function-norm, the following generalized definition of the -numerical radius is considered.
Definition 7.
Let be a continuous strictly increasing surjective function. Let be a sequence of Hilbert space operators in such that Let satisfy for each Define
- 1.
- 2.
- 3.
- The setis called the sequential joint -numerical range of .
- 4.
- The setis called the sequential joint -numerical range of .
- 5.
- For every , define
- 6.
- The non-negative numberis called the sequential joint -numerical radius of .
- 7.
- The non-negative numberis called the sequential joint -numerical radius of .
From a geometric viewpoint, when , the condition set consists of pairs of unit vectors satisfying the constraint together with the lower-bound conditions imposed by . Thus, the parameter restricts the admissible pairs of vectors by excluding those for which the relevant operator quantities are smaller than . As increases, these restrictions become stronger, and the corresponding condition set becomes more constrained.
The introduced notion contains several existing numerical radius concepts as particular cases. The following observations demonstrate that the proposed definition provides a common framework for various known generalizations.
- 1.
- If then coincides with the classical numerical radius of the operator A (see [4,33]).
- 2.
- If then coincides with the -numerical radius of the operator A (see [9]).
- 3.
- If then coincides with the Davis–Wielandt radius of the operator (see [34,35]).
- 4.
- If then coincides with the joint Euclidean numerical radius (joint p-numerical radius) of the n-tuple operators (see [14]).
- 5.
- If then coincides with the -numerical radius of the tuple operators (see [36]).
- 6.
- If then coincides with the q-numerical radius of the n-tuple operator (see [22]).
- 7.
- In case when the function numerical radius was previously considered in [37], where generalizations of known inequalities and structural results were obtained.
Remark 1.
The results in [36,37] are closely related to the present work. While [36] deals with the joint δ-numerical radius of finite operator tuples and [37] considers an f-numerical radius for finite operator tuples, the present paper combines the parameters f, δ, and q within a unified framework for sequences of operators. Thus, the main novelty of the present work is the simultaneous integration of these features into the sequential joint -numerical radius and the resulting properties and inequalities.
Throughout this article, for , the -numerical radius is identified with its value at , namely,
so that the consistency of the theory is preserved for nonpositive values of the parameter.
For any Hilbert space operators sequences and with we define
On the other hand, it is easy to verify that, for any sequence in and any with , the identity
holds for every . Moreover, for every ,
The following example illustrates the introduced concept in a concrete setting.
Example 1.
Let and consider the following operator sequence
and assume that It is clear that and
Hence,
Then, for from the relation
it is obtained
Similarly, since
and ,
Hence, for
Then, for any it is obtained that
In the following, some additional assumptions on the function f will be considered, together with an auxiliary set that will be used in later discussions.
Throughout this article, is assumed to be a continuous, strictly increasing, and surjective function. Under these assumptions, is obtained. Moreover, whenever is convex on , the inequality
is satisfied for every and . A function
is said to be geometrically convex if the inequality
is satisfied for all .
Similarly, a function is said to be multiplicative provided that
holds for all (see [38]).
In this case, or and or
The choice of the function f affects the tightness of the derived bounds, since different choices of f lead to different scalarizations and consequently to different estimates.
3. Sequential Joint -Numerical Radius
In this section, several fundamental properties of the sequential joint -numerical radius function associated with sequences of bounded linear operators on a Hilbert space are first examined. Subsequently, generalized formulas for the lower and upper bounds of this numerical radius function are established.
Theorem 1.
Let f be a convex function and be a sequence of Hilbert space operators, where for every , and let satisfy . Then, for
Proof.
Let be arbitrary. By monotonicity of the function we have
Hence, for every
which immediately yields
In the same manner, for every
and therefore
Consequently,
Combining (4) and (5) it is obtained that
Taking the supremum over all n gives
On the other hand, recall that the convexity of f implies that its inverse function is concave. Hence, by [37]
Moreover, the superadditivity of f [37] ensures that
for every and
Since is monotonically increasing, it follows that
for every and
Passing to the limit and using the continuity of , it follows that
for
Applying this inequality with we deduce that
for every
Theorem 2.
Let f be a multiplicative function and let where for each Then, for and every
- (1)
- (2)
- (3)
- (polarization formula).
Proof. (1)
Let Then
Taking the supremum over all we conclude that
- (2)
- Let ThenHenceOn the other hand, since f is multiplicative, we use the propertyThen, for everyTherefore,Combining this estimate with the inequality established in [31], it is obtained thatConsequently,
- (3)
- Combining assertions (1) and (2) of the theorem, we obtainwhich completes the proof.
□
Remark 2.
If the function f is not multiplicative, then assertions (2) and (3) of Theorem 2 become
and
respectively.
Corollary 1.
Let
If and , then assertions (1) and (3) of Theorem 2 reduce to the corresponding results established in [39], while assertion (2) reduces to a well-known joint numerical radius inequality; see [40].
As a consequence of Theorem 2, we obtain the following corollary. Assertion (1) recovers the result in [22], whereas assertion (2) recovers the result in [40].
Corollary 2.
Let for
- (1)
- If then
- (2)
- If then
Theorem 3.
Let and be two operators sequences on Then the following assertions hold.
- (1)
- If f is multiplicative, then for every and
- (2)
- Assume that f is a geometrically convex function. Then, for any operator sequences and on and for every the following inequality holds:In particular, if then
- (3)
- For every operator sequence on and every the following equality holds:Moreover, if then for anyFinally, if then for any is true
Proof. (1)
For any we have
Moreover, it follows directly from the definition of the corresponding set that
- (2)
- Since f is monotonically increasing, then for everyUsing the geometrically convexity of f (see [41]), we obtainHence, forThen,Similarly, by the same technique it can be shownTherefore, if thenandThus, it is obtained that
- (3)
- Let ThenFurthermore, by the definition of the setHence, from (12) and the definition of -numerical radius
This completes the proof. □
Theorem 3 contains several known results as special cases. In particular, we have the following consequences.
Corollary 3. (1)
If and then the result reduces to the result obtained in [33,42].
- (2)
- If for and then Theorem 3 recovers the result established in [39,43].
- (3)
- If for and and then the theorem yields the result obtained in [14].
- (4)
- If is a continuous increasing function and on and then the theorem has been established in [37].
- (5)
- If for on and then the theorem recovers the result obtained in [36].
- (6)
- If for on and then the theorem reduces to the corresponding result for the Davis–Wielandt radius obtained in [34,35].
- (7)
- If on and then the theorem reduces to the corresponding result in [44].
Now, we provide an useful auxiliary lemma.
Lemma 1
([45]). If then for any ,
Theorem 4.
Let f be a geometrically convex function. Then, for every operator sequence with and the following inequality hold
where
Proof.
Theorem 5.
Let f be a geometrically convex function and let be an operator sequence on Then for any
where satisfies
Proof.
A relationship between the -numerical radius of an operator and its real and imaginary parts is established in the following result.
Theorem 6.
Let f be a geometrically convex function. Then, for every operator sequence with and for every with the inequality
holds, where
Proof.
Since f is strongly increasing and geometrically convex, the midpoint convexity property (see [38]) implies that for every and every
Hence, from [41] it obtained that
Thus, the last relation can be written in form
for any
Theorem 7.
Let be a sequence of bounded linear operators on Then for every and every with
Proof.
Theorem 8.
Let f be a geometrically convex function, and let be a sequence of bounded linear operators on Then, for every and every with
Proof.
Since f is geometrically convex, for every and every we have
Summing over yields
Next, using the same technique, it follows that for every
Therefore,
This completes the proof. □
Theorem 9.
Let be an operator sequence on where for and let f be a geometrically convex function. Then, for every with the following inequalities hold:
for and
for
Proof.
Corollary 4.
If and then Theorem 9 reduces to the corresponding result obtained in [22].
Theorem 10.
Let f be a convex function and be a Hilbert space operator sequence, where for each . If and satisfies , then
Moreover, if f is multiplicative, then
Proof.
For arbitrary , where , with , and for each , choose
where and .
Moreover, since
where the latter is the standard inequality relating the operator norm and the numerical radius, by using (1) and (3) a direct computation yields
Hence, by (28), for every we have
Taking the supremum over all
□
Corollary 5.
As a particular case of Theorem 10, let
where , and let and . Then
which recovers the classical inequality of Kittaneh [5].
Corollary 6.
As a special case of Theorem 10, let
where , , and satisfies . Then Theorem 10 recovers the corresponding result established in [31].
4. Sectorial Case of Coordinate Operators
In this section, the sequential joint -numerical radius function will be investigated in the case when the all coordinate operators are sectorial, as defined in Definition 4.
Theorem 11.
Let be an operator sequence on and with Assume that f is convex and multiplicative. Then, the following assertions hold:
- (1)
- If and thenwhere and
- (2)
- If and thenwhere and
Proof. (1)
Let Since and we have
for every Hence, since f is multiplicative, then for every
On the other hand, we get for every
where
and
Summing over n and applying it follows that
Consequently, for
where
Now, let and Since f is multiplicative, by the midpoint property of f, we have
Then, it is clear that
where and similarly it can be shown that if and then where Therefore,
and
Then, from above calculations it is obtained that
This proves the first assertion.
- (2)
- The proof of the second assertion follows analogously.Thus, the theorem is completely proved.
□
Theorem 12.
Let and let be an operator sequences on Assume that for every the coordinate operators satisfy
Then,
- (1)
- For anywhere
- (2)
- For anywhere
- (3)
- For anywhere
- (4)
- For anywhere
Proof.
Let be arbitrary. Then,
On the other hand, since
and the tangent function is monotonically increasing in it follows that
Thus, for any
On the other hand, for the following inclusion holds
Then, from (32) and (33) it is obtained that
This proves the first assertion.
(2)–(4) The proofs are analogous to that of assertion (1).
Hence, the theorem is completely proved. □
Corollary 7.
In case when and the result in assertion (2) of Theorem 12 coincides with claim obtained in [46].
5. Conclusions
In this paper, we introduced the sequential joint -numerical radius for sequences of bounded linear operators on Hilbert spaces and developed its basic theory. We established fundamental properties of this generalized numerical radius, including its behaviour under scalar multiplication, adjoint operations, and decompositions involving the real and imaginary parts of operator sequences.
Furthermore, we obtained several upper and lower estimates for the sequential joint -numerical radius. These inequalities provide a unified framework that extends a number of existing numerical radius inequalities and recovers several known results as special cases. We also investigated the interaction between the introduced numerical radius and sectorial operator sequences, showing how geometric restrictions on the numerical ranges of the coordinate operators lead to additional bounds and structural relations.
The results presented here demonstrate that the sequential joint -numerical radius provides a flexible framework for studying operator sequences and their associated inequalities. The introduced concept connects generalized numerical radius theory with geometric properties of operator ranges and offers new perspectives for the analysis of sequences of Hilbert space operators.
Several directions remain open for future investigation. In particular, it would be interesting to study analogous constructions for broader classes of operators, including unbounded operators, operators on more general Banach spaces, and other generalized numerical radius functions. Further questions concerning applications to operator means, matrix inequalities, and related areas of functional analysis may also provide fruitful directions for future research.
Author Contributions
Conceptualization, Z.I.I. and P.I.A.; Validation, M.S.; Writing—original draft, Z.I.I. and P.I.A.; Writing—review and editing, Z.I.I., P.I.A. and M.S. All authors have read and agreed to the published version of the manuscript.
Funding
This work was supported by the Scientific Research Projects Coordination Unit of Karadeniz Technical University. Project number: FUA-2026-17423.
Data Availability Statement
No new data were created or analyzed in this study. Data sharing is not applicable to this article.
Conflicts of Interest
The authors declare no conflicts of interest.
References
- Axelsson, O.; Lu, H.; Polman, B. On Numerical Radius of Matrices and Its Application for Iterative Solution Methods, 2nd ed.; Department of Mathematics, Catholic University Nijmegen: Nijmegen, The Netherlands, 1993. [Google Scholar]
- Bonsall, F.F.; Duncan, J. Numerical Ranges II; Cambridge University Press: Cambridge, UK, 1971. [Google Scholar]
- Horn, R.A.; Johnson, C.R. Topics in Matrix Analysis; Cambridge University Press: Cambridge, UK, 1991. [Google Scholar]
- Halmos, P.R. A Hilbert Space Problem Book; Springer: New York, NY, USA, 1982. [Google Scholar]
- Kittaneh, F. Numerical radius inequalities for Hilbert space operators. Stud. Math. 2005, 168, 73–80. [Google Scholar] [CrossRef] [Scilit]
- Kittaneh, F. A numerical radius inequality and an estimate for the numerical radius of the Frobenius companion matrix. Stud. Math. 2003, 158, 11–17. [Google Scholar] [CrossRef] [Scilit]
- Stampfli, J.G. The norm of a derivation. Pac. J. Math. 1970, 33, 737–747. [Google Scholar] [CrossRef] [Scilit]
- Dekker, N.P. Joint Numerical Range and Joint Spectrum of Hilbert Space Operators. Ph.D. Thesis, University of Amsterdam, Amsterdam, The Netherlands, 1969. [Google Scholar]
- Cho, M.; Takaguchi, M. Boundary points of joint numerical ranges. Pac. J. Math. 1981, 95, 27–35. [Google Scholar] [CrossRef] [Scilit]
- Abu-Omar, A.; Kittaneh, F. A generalization of the numerical radius. Linear Algebra Appl. 2019, 569, 323–334. [Google Scholar] [CrossRef] [Scilit]
- Bourhim, A.; Mabrouk, M. Numerical radius and product of elements in C*-algebras. Linear Multilinear Algebra 2017, 65, 1108–1116. [Google Scholar] [CrossRef] [Scilit]
- Moradi, H.R.; Sababheh, M. More accurate numerical radius inequalities (II). Linear Multilinear Algebra 2021, 69, 921–933. [Google Scholar] [CrossRef] [Scilit]
- Moradi, H.R.; Sababheh, M. New estimates for the numerical radius. Filomat 2021, 35, 4957–4962. [Google Scholar] [CrossRef] [Scilit]
- Moslehian, M.S.; Sattari, M.; Shebrawi, K. Extensions of Euclidean operator radius inequalities. Math. Scand. 2017, 120, 129–144. [Google Scholar] [CrossRef] [Scilit]
- Omidvar, M.E.; Moradi, H.R. Better bounds on the numerical radii of Hilbert space operators. Linear Algebra Appl. 2020, 604, 265–277. [Google Scholar] [CrossRef] [Scilit]
- Omidvar, M.E.; Moradi, H.R. New estimates for the numerical radius of Hilbert space operators. Linear Multilinear Algebra 2021, 69, 946–956. [Google Scholar] [CrossRef] [Scilit]
- Sababheh, M. Heinz-type numerical radii inequalities. Linear Multilinear Algebra 2019, 67, 953–964. [Google Scholar] [CrossRef] [Scilit]
- Sababheh, M.; Moradi, H.R. More accurate numerical radius inequalities (I). Linear Multilinear Algebra 2021, 69, 1964–1973. [Google Scholar] [CrossRef] [Scilit]
- Sheikhhosseini, A.; Khosravi, M.; Sababheh, M. The weighted numerical radius. Ann. Funct. Anal. 2022, 13, 3. [Google Scholar] [CrossRef] [Scilit]
- Shebrawi, S.; Sababheh, M.; Moradi, H.R. Weighted inequalities for the numerical radius. Vietnam J. Math. 2023, 51, 363–377. [Google Scholar] [CrossRef] [Scilit]
- Yamazaki, T. On upper and lower bounds for the numerical radius and an equality condition. Stud. Math. 2007, 178, 83–89. [Google Scholar] [CrossRef] [Scilit]
- Feki, K.; Patra, A.; Rani, J.; Taki, Z. Joint q-numerical ranges of operators in Hilbert and semi-Hilbert spaces. Linear Multilinear Algebra 2026, 74, 294–316. [Google Scholar] [CrossRef] [Scilit]
- Marcus, M.; Andersen, P. Constrained extrema of bilinear functionals. Monatsh. Math. 1977, 84, 219–235. [Google Scholar] [CrossRef] [Scilit]
- Tsing, N.K. The constrained bilinear form and the c-numerical range. Linear Algebra Appl. 1984, 56, 195–206. [Google Scholar] [CrossRef] [Scilit]
- Li, C.-K.; Mehta, P.P.; Rodman, L. A generalized numerical range: The range of a constrained sesquilinear form. Linear Multilinear Algebra 1994, 37, 25–49. [Google Scholar] [CrossRef] [Scilit]
- Li, C.-K.; Nakazato, H. Some results on the q-numerical range. Linear Multilinear Algebra 1998, 43, 385–409. [Google Scholar] [CrossRef] [Scilit]
- Chien, M.-T.; Nakazato, H. Davis–Wielandt shell and q-numerical range. Linear Algebra Appl. 2002, 340, 15–31. [Google Scholar] [CrossRef] [Scilit]
- Chien, M.-T. The numerical radius of a weighted shift operator. RIMS Kôkyûroku 2012, 1778, 70–77. [Google Scholar]
- Chien, M.-T.; Nakazato, H. The q-numerical radius of weighted shift operators with periodic weights. Linear Algebra Appl. 2007, 422, 198–218. [Google Scholar] [CrossRef] [Scilit]
- Duan, R.; Feng, Y.; Ying, M. Perfect distinguishability of quantum operations. Phys. Rev. Lett. 2009, 103, 210501. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Moghaddam, S.F.; Mirmostafaee, A.K.; Janfada, M. q-Numerical radius inequalities for Hilbert space. Linear Multilinear Algebra 2024, 72, 751–763. [Google Scholar] [CrossRef] [Scilit]
- Ismailov, Z.I.; Silvestrov, S.; Ipek Al, P. Lower and upper bounds of joint (f,δ)-numerical radius functions. arXiv 2026, arXiv:2603.18405. [Google Scholar]
- Gustafson, K.E.; Rao, D.K.M. Numerical Range: The Field of Values of Linear Operators and Matrices; Springer: New York, NY, USA, 1997. [Google Scholar]
- Davis, C. The shell of a Hilbert-space operator. Acta Sci. Math. 1968, 29, 69–86. [Google Scholar]
- Wielandt, H. On eigenvalues of sums of normal matrices. Pac. J. Math. 1955, 5, 633–638. [Google Scholar] [CrossRef] [Scilit]
- Ismailov, Z.I.; Otkun Çevik, E. Some inequalities for bounds of joint δ-numerical radius and joint δ-Crawford number functions. Linear Multilinear Algebra 2026, 74, 192–212. [Google Scholar] [CrossRef] [Scilit]
- Alomari, M.W.; Sababheh, M.; Conde, C.; Moradi, H.R. Generalized Euclidean operator radius. Georgian Math. J. 2024, 31, 369–380. [Google Scholar] [CrossRef] [Scilit]
- Niculescu, C.P.; Persson, L.-E. Convex Functions and Their Applications: A Contemporary Approach; CMS Books in Mathematics; Springer: New York, NY, USA, 2006. [Google Scholar]
- Jana, S.; Bhunia, P.; Paul, K. Euclidean operator radius inequalities of d-tuple operators and operator matrices. Math. Slovaca 2024, 74, 947–962. [Google Scholar] [CrossRef] [Scilit]
- Drnovšek, R.; Müller, V. On joint numerical radius II. Linear Multilinear Algebra 2014, 62, 1197–1204. [Google Scholar] [CrossRef] [Scilit]
- Mulholland, H.P. On generalizations of Minkowski’s inequality in the form of a triangle inequality. Proc. Lond. Math. Soc. 1950, 51, 294–307. [Google Scholar] [CrossRef] [Scilit]
- Bhunia, P.; Dragomir, S.S.; Moslehian, M.S.; Paul, K. Lectures on Numerical Radius Inequalities; Infosys Science Foundation Series; Springer: New York, NY, USA, 2022. [Google Scholar]
- Dragomir, S.S. Upper bounds for the Euclidean operator radius and applications. J. Inequal. Appl. 2008, 2008, 472146. [Google Scholar] [CrossRef] [Scilit]
- Gau, H.; Wu, P.Y. Numerical Ranges of Hilbert Space Operators; Cambridge University Press: Cambridge, UK, 2021. [Google Scholar]
- Berezansky, Y.M.; Sheftel, Z.G.; Us, G.F. Functional Analysis; Birkhäuser: Basel, Switzerland, 1966. [Google Scholar]
- Sammour, S.A.; Kittaneh, F.; Sababheh, M. A geometric approach to numerical radius inequalities. Linear Algebra Appl. 2022, 652, 1–17. [Google Scholar] [CrossRef] [Scilit]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.